| Description |
1 online resource (electronic texts (378 pages)) |
| Contents |
1. Higher order neural network group-based adaptive tolerance trees -- 2. Higher order neural networks for symbolic, sub-symbolic and chaotic computations -- 3. Evolutionary algorithm training of higher order neural networks -- 4. Adaptive higher order neural network models for data mining -- 5. Robust adaptive control using higher order neural networks and projection -- 6. On the equivalence between ordinary neural networks and higher order neural networks -- 7. Rainfall estimation using neuron-adaptive higher order neural networks -- 8. Analysis of quantization effects on higher order function and multilayer feedforward neural networks -- 9. Improving sparsity in kernelized nonlinear feature extraction algorithms by polynomial kernel higher order neural networks -- 10. Analysis and improvement of function approximation capabilities of pi-sigma higher order neural networks -- 11. Dynamic ridge polynomial higher order neural network -- 12. Fifty years of electronic hardware implementations of first and higher order neural networks -- 13. Recurrent higher order neural network control for output trajectory tracking with neural observers and constrained inputs -- 14. Artificial higher order neural network training on limited precision processors -- 15. Recurrent higher order neural observers for anaerobic processes -- 16. Electric machines excitation control via higher order neural networks -- 17. Higher order neural networks |
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18. Identification of nonlinear systems using a new neuro-fuzzy dynamical system definition based on high order neural network function approximators -- 19. Neuro -- fuzzy control schemes based on high order neural network function approximators -- 20. Back-stepping control of quadrotor -- 21. Artificial tactile sensing and robotic surgery using higher order neural networks -- 22. A theoretical and empirical study of functional link neural networks (FLANNs) for classification |
| Summary |
This book introduces Higher Order Neural Networks (HONNs) to computer scientists and computer engineers as an open box neural networks tool when compared to traditional artificial neural networks. Since HONNs are open box models, they can be easily used in information science, information technology, management, economics, and business. This book details the techniques, theory and applications essential to engaging and capitalizing on this developing technology--Provided by publisher |
| Analysis |
Higher order neural networks |
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Evolutionary algorithms |
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Higher order neural network models for data mining |
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Ordinary neural networks vs. higher order neural networks |
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Simulation and modeling |
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Artificial higher order neural networks for computer engineering |
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Hardware implementations of first and higher order neural networks |
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Neural network theory and applications |
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Neuro fuzzy control schemes and systems |
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Artificial tactile sensing and robotic surgery |
| Bibliography |
Includes bibliographical references and index |
| Notes |
Print version record |
| Subject |
Neural networks (Computer science)
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Computer science -- Data processing
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Computer engineering -- Data processing.
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Computer engineering -- Data processing
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Neural networks (Computer science)
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| Form |
Electronic book
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| Author |
Zhang, Ming, 1949 July 29-
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IGI Global.
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| LC no. |
2009050067 |
| ISBN |
9781615207121 |
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1615207120 |
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